COIN：面向不确定性感知的自适应资源超售策略的机会约束模仿学习
COIN: Chance-Constrained Imitation Learning for Uncertainty-aware Adaptive Resource Oversubscription Policy
AI总结：
本文提出机会约束模仿学习框架COIN，利用随机约束与集成价值函数处理遥测数据中的偶然不确定性，在云资源超售中实现约3-4倍的效率与安全性提升。
摘要：
我们针对在存在不确定性的情况下学习安全且鲁棒的决策策略这一挑战展开研究，具体背景是自适应资源超售这一真实科学问题，目标是在确保防范资源拥塞风险安全性的同时提升资源效率。传统的监督预测或预报模型在学习自适应策略方面效果不佳，而标准的在线优化或强化学习难以部署到真实系统上。模仿学习（IL）等离线方法是理想选择，因为我们可以直接利用历史资源使用遥测数据。然而，此类遥测数据中固有的偶然不确定性是一个关键瓶颈。我们通过提出新颖的机会约束模仿学习框架来解决这一问题，该框架通过资源拥塞风险上的随机（机会）约束与集成价值函数的组合，以有原则的方式确保对不确定性的隐式安全性。这在许多超售场景（包括云服务中的资源管理）中带来了资源效率和安全性的显著提升（约3-4倍）。
英文摘要：
We address the challenge of learning safe and robust decision policies in presence of uncertainty in context of the real scientific problem of adaptive resource oversubscription to enhance resource efficiency while ensuring safety against resource congestion risk. Traditional supervised prediction or forecasting models are ineffective in learning adaptive policies whereas standard online optimization or reinforcement learning is difficult to deploy on real systems. Offline methods such as imitation learning (IL) are ideal since we can directly leverage historical resource usage telemetry. But, the underlying aleatoric uncertainty in such telemetry is a critical bottleneck. We solve this with our proposed novel chance-constrained imitation learning framework, which ensures implicit safety against uncertainty in a principled manner via a combination of stochastic (chance) constraints on resource congestion risk and ensemble value functions. This leads to substantial ($\approx 3-4\times$) improvement in resource efficiency and safety in many oversubscription scenarios, including resource management in cloud services.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
